Frontiers in Operations: News Event-Driven Forecasting of Commodity Prices

Sunandan Chakraborty (), Srikanth Jagabathula (), Lakshminarayanan Subramanian () and Ashwin Venkataraman ()
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Sunandan Chakraborty: Luddy School of Informatics, Computing and Engineering, Indiana University, Indianapolis, Indiana 46202
Srikanth Jagabathula: Stern School of Business, New York University, New York, New York 10012
Lakshminarayanan Subramanian: Courant Institute of Mathematical Sciences, New York University, New York, New York 10012
Ashwin Venkataraman: Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080

Manufacturing & Service Operations Management, 2024, vol. 26, issue 4, 1286-1305

Abstract: Problem definition : Commodity prices have exhibited significant volatility in recent times, which poses an exogenous risk factor for commodity-processing and commodity-trading firms. Accurate commodity price forecasts can help firms leverage data-driven procurement policies that incorporate the underlying price volatility for financial and operational hedging decisions. However, historical prices alone are insufficient to obtain reasonable forecasts because of the extreme volatility. Methodology/results : Building on the hypothesis that commodity prices are driven by real-world events, we propose a method that automatically extracts events from news articles and combines them with price data using a neural network-based predictive model to forecast prices. In addition to achieving a high prediction accuracy that outperforms several benchmarks (by up to 13%), our proposed model is also interpretable , which allows us to identify meaningful events driving the price fluctuations. We found that the events frequently associated with major fluctuations in the price include “natural,” “hike,” “policy,” and “elections,” all of which are known drivers of price change. We used a corpus containing about 1.6 million news articles of a major Indian newspaper spanning 15 years and daily prices of four crops (onion, potato, rice, and wheat) in India to perform this study. Our proposed approach is flexible and can be used to predict other time series data, such as disease incidence levels or macroeconomic indicators, that are also influenced by real-world events. Managerial implications : Firms can leverage price forecasts from our system to design inventory and procurement policies in the face of uncertain commodity prices. Commodity merchants can also use the forecasts to design optimal storage policies for physical trading of commodities when prices are volatile. Our findings can also significantly impact policymakers, who can leverage the information of impending price changes and associated events to mitigate the negative effects of price shocks.

Keywords: forecasting; price volatility; food supply chain; textual data; procurement (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:26:y:2024:i:4:p:1286-1305

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